Across our global cities, a diverse range of plants and dietary ingredients nourish people and enliven tables, kitchens, markets, gardens, bodies, hearts, and souls. The global presence of nutritious, delicious and culturally significant foods -- such as amaranth, bitter melon, chayote squash, daikon radish, eddo, fenugreek, gongura, hakurei turnip, ivy gourd, jute leaves, kai lan, lady finger, Malabar spinach, Napa cabbage, okra, pak choi, quince, rakkyo, shiitake, tamarind, urad bean, verdolaga, winter melon, xa lach xoong, zucchini blossoms -- emerges from the lived histories, recipes, kitchens, and gardens of the world's migrants. The diversity of foods in our modern world is directly due to the cultures, histories, ecohealth actions, and growing spaces connected to migrants. Yet, the wider significance of the migrant foodscapes of global cities such as Toronto (Canada) and London (UK) for the Anthropocene era's food sovereignty, agro-biodiversity, community health, and sustainability, is yet to be fully understood. With over a billion people on the move, international migration and migrants constitute a continuing flashpoint for debate. In current public discourse, migrants are celebrated at times, but more often, blamed and stigmatized for social ills, particularly at times of perceived crisis. This is a crucial moment in world history, as global migration increases due to climate change and geopolitics, and as cities grapple with meeting Sustainable Development Goals. With this unique Canada-UK opportunity to mobilize and disseminate scholarship toward enhanced academic and public policy knowledge outcomes, this collaborative, transnational program will mobilize, synthesize, and disseminate interdisciplinary, community-based research on how migrant foodscapes, especially the ethnocultural food gardens and connected community socio-ecological actions of urban migrants, contribute to the sustainable development of global cities and communities. Ours is a culturally, linguistically, racially, and gender diverse Canada-UK team that works at the interdisciplinary intersections of food and environmental studies, cultural studies, area studies, health, and sustainability studies. Team members have extensive experience training students and emerging scholars in participatory research approaches, social sciences and humanities research methods, multi-media research and digital humanities, in mentoring toward professional networking, and in nurturing them in community-engaged activities. This grant will enable training and mentoring opportunities for numerous students, both those directly involved with knowledge mobilization, and those who indirectly benefit from its outcomes. Future leaders will benefit from this exposure to equity-driven social policy in practice, via the program's inclusive lens on diverse urban communities that are often invisibilized. This is a transnational opportunity for emerging scholars (Yue, Elton, Rohel) to collaborate internationally with senior/mid-career scholars (Bender, Pilcher, Sharma, Song). The team is formed by experts from multiple universities, including UCL, University of Toronto, Metropolitan University of Toronto and Durham University. Team members have a strong history of engagement on transdisciplinary scholarly collaborations, and in community partnerships toward public policy outcomes. Together, they leverage this knowledge synthesis opportunity on cultures and histories to train future generations for both countries, produce academic scholarship, foster future collaboration and diverse community ties.

Unmanned systems are growing fast, and there is an urgent need to improve the robustness and efficiency of such systems. Quadrotors are one prime example, which can be used in a variety of different domains. This includes infrastructure inspection, disaster management, search and rescue, precise agriculture, and package delivery. The government has shown a huge interest in autonomous vehicles. The release of the Future of Transport: rural strategy highlights the opportunities for drones to make deliveries in rural or isolated towns and to help reduce pollution. Furthermore, reports have shown the self-driving vehicle industry to be worth nearly £42 billion by 2035. Autonomous vehicles rely on highly accurate localization and mapping techniques which can be very difficult in cluttered and dynamic scenes. Dead-reckoning based methods which rely on previous estimates work in these scenarios but fall victim to propagated error which leads to inaccuracies in the long run. This has led to research in the loop closure which utilizes previously seen landmarks to re-localize the vehicle. The most common form of self-localization within autonomous vehicles comes from Simultaneous Localization and Mapping, which is a technique that utilizes detected landmarks and control inputs to estimate the position and orientation of the vehicle within a generated map. The assumption of static landmarks however still provides an issue within the previously mentioned dynamic environments, as static landmarks are needed to be filtered from dynamic landmarks. Dynamic-SLAM methods modify the existing method by providing this filtering technique but still lack robustness when dynamic objects fill up the majority of the environment. We hope to tackle this problem using data-driven approaches. Reinforcement learning has been shown as a viable solution for navigation within mapless and dynamic environments. We hope to train the reinforcement learning agent, through a series of simulation environments, the ability to navigate in a dynamic and cluttered environment using onboard camera depth sensors. Building on work already done but that would not have been able to take place during the PhD. An experimental quadrotor has already been developed and we hope to utilize this within Ryerson University's drone arena to validate the proposed hypothesis. The key outputs of this project will be the development of reinforcement learning techniques to navigate within a mapless environment to aid with the mapping process in a dynamic scene. This novel technique provides an alternative solution to the current advances in dynamic-SLAM. We hope that reinforcement learning-based techniques will improve dynamic-SLAM's ability to be utilized. Furthermore, such a technical solution can be easily applied to industrial applications and is supposed to, in practice, fill the gap between autonomous control and popular artificial intelligence techniques We believe that the proposed research brings the strength of robotics research from our partners in Canada to significantly improve the accessibility of AI techniques in autonomous robotics, and further strengthen the UK's role as the global leader in the creation of industrial autonomy solutions. Such a role aligns with the current UK research roadmap, with at least £800 million to ensure the UK can gain a competitive advantage in the creation of artificial intelligence and industrial autonomy.

Link4Skills is a global research and innovation project on skill shortages. The acronym reflects the objectives of the call by linking for/4 fair skill matching. It embeds 4 processes of responding to skill shortages: re/up skilling of established populations (incl. migrants and inactive women), raising wages, automation and migration. It considers 4 continents: Europe, Africa, Asia and America, where skill shortages and skill flows will be analysed. It develops the AI-Assisted Skill Navigator for stakeholders from employment, vocational training organisations in origins and destinations. Link4Skills will scrutinize: (a) how to identify the existing and emerging required skills in changing labour markets?; (b) how the EU should respond to skill shortages?; (c) how to recruit the required skills from various pools either from the existing workforce (including established migrant populations and inactive women) also supported by automation, and from the workforce from non-EU countries? The project combines data on skill gaps and matching in the EU with analyses about human capital in origins; investigates emerging and established migration skill corridors between EU and India, Morocco, Ghana, Nigeria, Philippines, Indonesia, and Ukraine, in order to make enriched inventories of skill partnerships. The project achieves its aims via econometric microsimulations based on EU databases, combining skill supply and demand, and by data collections and stakeholders’ expertise oversees. The knowledge will be nested in the AI-Assisted Skill Navigator (TRL5) which is a Knowledge-Based Expert System, that goes beyond existing policy dashboards. It is an open access system available to public. It is co-created by labour market stakeholders in every partner country. Partners will take care about stakeholders’ involvements in the project, by enhancing tailor-made communication and dissemination. The project will also produce Link4Skill Podcast Series and academic outlets.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.